Space Agency

The innovation behind these transformations is machine learning, a kind of algorithm that ingests and analyzes tons of data to find common patterns, and turn those patterns into predictions and actions. The practice, known as "precision farming," uses real-time and historical data along with machine learning algorithms to take specific actions for smaller areas and time increments instead of performing the same thing for a very large area in a routine-based manner. Deep learning and computer vision algorithms analyze the collected data to learn and report when something important is happening. These tasks can be as easy as controlling irrigation on different parts of the field based on humidity data obtained from sensors, or notifying distribution partners based on the amount and time of yield expected.

The innovation behind these transformations is machine learning, a kind of algorithm that ingests and analyzes tons of data to find common patterns, and turn those patterns into predictions and actions. The practice, known as "precision farming," uses real-time and historical data along with machine learning algorithms to take specific actions for smaller areas and time increments instead of performing the same thing for a very large area in a routine-based manner. Deep learning and computer vision algorithms analyze the collected data to learn and report when something important is happening. These tasks can be as easy as controlling irrigation on different parts of the field based on humidity data obtained from sensors, or notifying distribution partners based on the amount and time of yield expected.

There are many projects using computer vision systems, machine learning and large data sets to hopefully make a difference to our oceans and gain the knowledge to have a real impact on future sustainability. Scientists, environmentalists and other organisations involved with saving coral reefs will be able to use this information to learn how pollution, costal activities, fishing and global warming are affecting them and act upon these findings accordingly. These tiny plants float on the ocean's surface and are responsible for producing most of the oxygen we breathe, consuming carbon dioxide and providing a fundamental food source for many species. Using sensors to collect data to inform machine learning models would be an approach that could help us better understand how the ecosystem works and provide a base for experimentation.

This was in contrast to previous stints at Start-Ups funded by NASA / NIST where a broader set of Machine Learning (ML) methods including SVMs, NNs, Random or Gradient Boosting Trees were regularly applied. It's a no-brainer that Deep Learning has been the most visible new age Machine Learning algorithm developed in the last 5 years, with marked success in generating insights from Large Unstructured Datasets of Images, Audio and Text. Modern end-to-end Big Data Platforms available today provide the computational power to train new age ML algorithms and streamline their deployment. Bio: Jayesh Ametha is Retail Banking Professional with 15 years in Business Strategy, Credit Risk and Advanced Analytics.

It's not just the temperature that makes Venus a particularly nasty place for computers--the pressure at the surface is around 90 atmospheres, equivalent to the pressure 3,000 feet down in Earth's ocean. The majority of ideas for Venus surface exploration have essentially been the same sort of thing that the Soviets did with the Venera probes: Stuffing all the electronics inside of an insulated container hooked up to a stupendously powerful air conditioning system, probably driven by some alarmingly radioactive plutonium-powered Stirling engines. With funding from the NASA Innovative Advanced Concepts (NIAC) program, the JPL team wants to see whether it might be possible to build a Venus exploration rover without conventional sensors, computers, or power systems. A Strandbeest operates off just a couple simple sensors, which control whether the legs move backwards or forwards, and it has built-in logic to avoid soft sand and water.

According to the NOAA's space weather prediction center, 'G4 (Severe) geomagnetic storm levels were observed at 2350 UTC (19:50 Eastern) on 07 September, again at 0151 UTC (21:51 Eastern) on 08 September and 1304 UTC (09:04 Eastern) due to effects from a coronal mass ejection. These radiation flares, which can disrupt communications satellites, GPS and power grids, were detected and captured by Nasa's Solar Dynamics Observatory (SDO) satellite. Yesterday's category X eruptions triggered a massive coronal mass ejection (pictured), which was also captured by SDO (credit: Nasa) Solar flares are giant explosions on the sun that send energy, light and high speed particles into space. These radiation flares, which can disrupt communications satellites, GPS and power grids, were detected and captured by Nasa's Solar Dynamics Observatory (SDO) satellite (artist's impression) Solar flares and CMEs (pictured) can damage satellites and have an enormous financial cost.

On Sept. 1, 1902, the world was treated to a truly ridiculous but also amazing short silent film that definitely holds up more than a century later. A Trip to the Moon, directed by Georges Méliès, takes viewers on a journey with astronomers in wizard hats and a pretty goofy moon with a face. Plus, according to NASA, it's the first science fiction movie with special effects. You have to see this news station's ridiculous edit of a crime victim's statement Try not to cry watching this emotional'Game of Thrones' video about Jon Snow

"Rather than using machine learning to get people to click ads or maximize page views, I decided solving problems in climate science was a better use of my skills and time." "Rather than using machine learning to get people to click ads or maximize page views, I decided solving problems in climate science was a better use of my skills and time," Vandal said. "These downscaled datasets will be of immense value to climate researchers and eco-climatic modelers who want to study anything from the impact of ecosystems to changes in climate for future warming scenarios," said Sangram Ganguly, one of the study's co-authors and a senior research scientist at the Bay Area Environmental Research Institute at the NASA Ames Research Center. "The computer science field changes really fast," Auroop Ganguly said.

There's a chance clouds will obscure your view of the total solar eclipse on Aug. 21, 2017, according to this map of typical cloudiness on that date through history. If clouds obscure this total solar eclipse, there won't be another one visible in the lower 48 states until April 2024, according to NASA. There will also be an annular solar eclipse, when the moon passes in front of the sun but not at the right distance to completely block it out, viewable in the continental U.S. in 2023. The last time a total solar eclipse moved through the mainland United States was in February 1979.